SBIR-STTR Award

This SBIR Phase II innovation is twofold. First, advanced natural language processing techniques are used to analyze the content of websites and identify features of the text that determine processing difficulty for the individual student. After the initial evaluation of text difficulty, the system becomes adaptive and automatically updates its decision for the suitability of a website by keeping track of the user's reading experience. This machine learning technology enables differentiated learning and, thus, enables fair access of web content to English Language Learners and special education groups. Second, the content of websites is characterized by advanced text analysis and classification to detect school subject areas. Elementary school students, for example, will find science content written at a level that they can comprehend, thus, being enabled to laser focus their attention to accessible content and engage with it critically. The proposed technology addresses the critical problem of information management and helps teachers teach their students the skills that they need to find, evaluate, and take advantage of available information in any context.

The broader/commercial impact of the proposed technology will be in the integration of the recently articulated common core standards (21st century learning) in the K-12 classroom. The development of the proposed technology will offer unprecedented opportunity for differentiated learning and give students a life-long tool to help them understand and critically engage with the information available to them. Early integration of information literacy and inquiry based learning is enabled not only by identifying age appropriate resources but, also, by offering a total project management platform. Teachers and curriculum developers will save valuable time and effort because they will have community space to find curated up-to-the-minute resources and core projects, which they can edit, save, and share, assign them to students, and receive submitted work, all in one window. Students, too, have personal space to keep notes, automatically keep track of resources for later review and citation, collaborate, prepare, and submit their reports. The platform can integrate with school library resources, thus, becoming a powerful tool for librarians. Application Programming Interfaces can be developed for collaboration with publishers enabling them to smoothly integrate core standards in their curricula and update them for 21st century learning.

This Small Business Innovation Research Phase II project engages K-12 students in inquiry-based learning not only in STEM education but across the curriculum. The coming of the information age has mandated radical changes in the skills set required for 21st century. To teach these skills, as articulated in the Common Core Standards, teachers are expected to target students? ability to conduct short as well as more sustained research projects. The Internet is not only the obvious go-to place for teaching these skills but one of the reasons for mandating them. It is the easy access to vast amounts of information that has made it possible for everyone to conduct research, analyze data and build new knowledge. The problem, however, is that the Internet is not a library with reliable and catalogued information that can be searched by topic and, in school libraries, by reading level. The development of the proposed technology, a personalized librarian on-demand, solves this problem. It offers unprecedented opportunity for learners of all ages to engage in inquiry, it enables differentiated learning and gives learners a life-long tool to help them understand and critically engage with digital information. The target market is not limited to the U.S. The developed linguistic algorithms apply to non-English, allowing early targeting of the international market by building web curators for other languages. This project blends research on linguistics, psychology of reading, natural language processing (NLP) and machine learning. Innovative NLP technology is used to detect age-appropriate sites that are relevant to the curriculum. The developed technology analyzes the reading difficulty and thematic content of websites automatically in real time search. The purpose of the system is to make adaptive individualized recommendations that match the student's reading level and are appropriate for her current familiarity with the topic. The patent pending adaptive component is built using machine learning and collaborative filtering methods. The resulting research tool, a personalized web curator, is based on novel methods for modeling the student's familiarity with the topic of web resources. These methods are extended to include novel analysis of video content. Unlike other adaptive educational products, the system's adaptive recommendations do not rely on obtaining test scores from the user and corresponding them to pre-labeled, leveled data. The complete solution includes an integrated gamified experience that helps the students acquire basic and advanced research skills and develop the habit of applying them consistently. The project team includes educational technology experts from academia and schools and conducts extensive studies evaluating learning benefits by the afforded differentiation in instruction, including special education programs and English Language Learners.